Validation of physical activity tracking via android smartphones compared to actigraph accelerometer: Laboratory-based and free-living validation studies
Open accessAbstract
BACKGROUND: There is increasing interest in using smartphones as stand-alone physical activity monitors via their built-in accelerometers, but there is presently limited data on the validity of this approach. OBJECTIVE: The purpose of this work was to determine the validity and reliability of 3 Android smartphones for measuring physical activity among midlife and older adults. METHODS: A laboratory (study 1) and a free-living (study 2) protocol were conducted. In study 1, individuals engaged in prescribed activities including sedentary (eg, sitting), light (sweeping), moderate (eg, walking 3 mph on a treadmill), and vigorous (eg, jogging 5 mph on a treadmill) activity over a 2-hour period wearing both an ActiGraph and 3 Android smartphones (ie, HTC MyTouch, Google Nexus One, and Motorola Cliq). In the free-living study, individuals engaged in usual daily activities over 7 days while wearing an Android smartphone (Google Nexus One) and an ActiGraph. RESULTS: Study 1 included 15 participants (age: mean 55.5, SD 6.6 years; women: 56%, 8/15). Correlations between the ActiGraph and the 3 phones were strong to very strong (ρ=.77-.82). Further, after excluding bicycling and standing, cut-point derived classifications of activities yielded a high percentage of activities classified correctly according to intensity level (eg, 78%-91% by phone) that were similar to the ActiGraph's percent correctly classified (ie, 91%). Study 2 included 23 participants (age: mean 57.0, SD 6.4 years; women: 74%, 17/23). Within the free-living context, results suggested a moderate correlation (ie, ρ=.59, P<.001) between the raw ActiGraph counts/minute and the phone's raw counts/minute and a strong correlation on minutes of moderate-to-vigorous physical activity (MVPA; ie, ρ=.67, P<.001). Results from Bland-Altman plots suggested close mean absolute estimates of sedentary (mean difference=-26 min/day of sedentary behavior) and MVPA (mean difference=-1.3 min/day of MVPA) although there was large variation. CONCLUSIONS: Overall, results suggest that an Android smartphone can provide comparable estimates of physical activity to an ActiGraph in both a laboratory-based and free-living context for estimating sedentary and MVPA and that different Android smartphones may reliably confer similar estimates.
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Smartphones are capable of accurately measuring physical activity in controlled and uncontrolled settings.
“Smartphones are capable of accurately measuring physical activity in controlled and uncontrolled settings (30)”
From Which Components of a Smartphone Walking App Help Users to Reach Personalized Step Goals? Results From an Optimization Trial (2020) · cites this paper as [30]
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- Authors
- Hekler, E. B., Buman, M. P., Grieco, L., Rosenberger, M., Winter, S. J., Haskell, W., & King, A. C.
- Affiliations
- Arizona State University (US)
- Stanford University (US)
- Corresponding author
- Hekler, Eric B
- Venue
- JMIR mhealth and uhealth
- Publisher
- JMIR Publications Inc.
- Published
- 2015-04-15
- Volume
- 3
- Issue
- 2
- Pages
- e36
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC4414958
- OpenAlex
- W2124775720
- Cited by
- 137
- References
- 37
- Citations by year
- 2026: 7 · 2025: 3 · 2024: 6 · 2023: 17 · 2022: 8 · 2021: 12 · 2020: 28 · 2019: 13 · 2018: 15 · 2017: 15 · 2016: 8 · 2015: 5
- FWCI
- 10.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Heart, Lung, and Blood Institute (RC1HL099340)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- physical activity monitoring, smartphone accelerometer, Android smartphones, ActiGraph, accelerometer validity, reliability statistics, moderate-to-vigorous physical activity, sedentary behavior, activity intensity classification, free-living physical activity, middle-aged and older adults, Bland-Altman agreement, wearable devices, step count
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